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How can I improve OCR accuracy when dealing with non-English languages and characters?
Tesseract OCR supports over 100 languages, including many non-English languages, and can be configured to recognize multiple languages simultaneously.
The quality of the scanned image greatly affects OCR accuracy, with high-resolution scans (at least 300 dpi) producing better results.
Image binarization, which separates text from the background, can improve OCR accuracy by up to 20%.
fonts with complex scripts, such as Arabic or Devanagari, can reduce OCR accuracy by up to 50%.
Pre-processing techniques like image thresholding, deskewing, and despeckling can improve OCR accuracy by up to 30%.
Tesseract OCR uses a combination of language models and dictionary-based approaches to recognize non-English languages.
The TextBlob package in Python can perform language detection and translation, making it a useful tool for non-English OCR.
OCR accuracy can be improved by up to 15% by using a custom-trained language model tailored to the specific language and font used.
Non-English languages like Chinese, Japanese, and Korean require specialized OCR engines due to their complex character sets.
The font type and size used in the original document can affect OCR accuracy, with serif fonts and larger font sizes producing better results.
OCR engines can struggle with handwritten text, reducing accuracy by up to 40%.
Document layout analysis (DLA) can improve OCR accuracy by up to 20% by identifying and isolating individual text regions.
The Tesseract OCR engine uses a two-pass approach, first identifying text regions and then recognizing individual characters.
OCR accuracy can be improved by up to 10% by using a combination of multiple OCR engines.
Machine learning-based approaches, such as convolutional neural networks (CNNs), can improve OCR accuracy by up to 25% for non-English languages.
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